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Relations between Theory of Mind and Indirect and Physical Aggression in Kindergarten: Evidence of the Moderating Role of Prosocial Behaviors

2009· article· en· W1951134092 on OpenAlexaffabout
Annie Renouf, Mara Brendgen, Sophie Parent, Frank Vitaro, Philip David Zelazo, Michel Boivin, Ginette Dionne, Richard E. Tremblay, Daniel Pérusse, Jean R. Séguin

Bibliographic record

VenueSocial Development · 2009
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversité LavalUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à Montréal
Fundersnot available
KeywordsProsocial behaviorAggressionPsychologyDevelopmental psychologyContext (archaeology)Theory of mindVocabularyCognition

Abstract

fetched live from OpenAlex

Abstract The present study examined the association between theory of mind and indirect versus physical aggression, as well as the potential moderating role of prosocial behavior in this context. Participants were 399 twins and singletons drawn from two longitudinal studies in Canada. At five years of age, children completed a theory of mind task and a receptive vocabulary task. A year later, teachers evaluated children's indirect and physical aggression and prosocial behavior. Indirect aggression was significantly and positively associated with theory of mind skills, but only in children with average or low levels of prosocial behavior. Physical aggression was negatively associated with prosocial behavior but not with theory of mind. Each analysis included gender, receptive vocabulary, and the respective other subtype of aggression as control variables. These results did not differ between girls and boys or between twins and singletons. Theoretical and clinical implications of these findings are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.316
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations96
Published2009
Admission routes2
Has abstractyes

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